AI Impact on Profitability per Employee

Unprecedented inflation, pricing pressures affecting both margins and consumer demand, and changing work habits are forcing companies to rethink productivity.?

In the face of economic headwinds, companies are accelerating investments in artificial intelligence innovation to buoy corporate profits and prepare for recessionary shocks.??According to Larry Summers, “… given the current inflation of nearly 8% and unemployment below 4%, historical evidence suggests a very substantial likelihood of recession over the next 12 to 24 months.”

Since 1955, there has never been a quarter with average inflation above 4% and unemployment below 5% that was not followed by a recession within the next two years.

Will progressive companies defy historical odds and fare better by leveraging the power of artificial intelligence???The world of work is experiencing a significant artificial intelligence transformation that promises to change the course of work history.

Reinventing Work with Artificial Intelligence

The?untapped?power of big data is the information about employees’ work behavior – data breadcrumbs they leave behind as they work.??They tell the non-fiction story of work.??Artificial intelligence creates actionable insights about how work gets done and can be optimized by processing production data.?

Purpose-built productivity algorithms – like those in?enaible’s AI-driven Productivity Platform?– learn the?connection between employees’ work and outcomes.??They inform how to expand capacity,?reduce errors, speed up execution, and maximize profitability per employee.

Companies are more productive with employees and algorithms working together.

Until artificial intelligence arrived on the scene, it made sense that it was impossible to evaluate and measure productivity comprehensively.??No single person can process all the data signals to predict outcomes with any degree of certainty.??Work is far more complex and less consistent than meets the eye.

Now, AI-driven Productivity Platforms allow companies to derive previously undiscovered productivity insights from their data.??They accurately learn how employees work, comprehensively measure the relationship between work and outputs, and provide actionable insights to optimize productivity.??Yet, surprisingly 68% of enterprise data goes?unused?for analytics, according to Seagate’s?Rethink Data.

Human-machine systems offer an incredible possibility to make the future of work more productive, profitable, and prosperous.??Traditional management methods will fail by comparison because the value from data is more significant.

Disrupting?Average

As we dive deeper into an era driven by big data, most of the ways we think about work change?dramatically.??Traditional management approaches reduce highly variant work realities into aggregate insights.??But productivity is more than isolated averages.??

?“For instance, Adam Smith and Karl Marx were wrong or at least had only half the answers.??Why???Because they talked about markets and classes, but those are aggregates.??They're averages,” commented?Sandy Pentland, MIT’s Director of Connection Science.

“While it may be useful to reason about the averages, social phenomena are really made up of millions of small transactions between individuals.??There are patterns in those individual transactions that are not just averages.??You need to get down into these new patterns, these micro-patterns, because they don't just average out to the classical way of understanding society.??We're entering a new era of social physics, where it's the details of all the particles—the you and me—that actually determine the outcome.

Reasoning about markets and classes may get you half of the way there, but it's this new capability of looking at the details, which is only possible through Big Data, that will give us the other 50 percent of the story.”?

The same holds for corporate productivity.

Key performance indicators are only partial measures that neither comprehensively explain how people are working nor how they can get better.??Micro-work patterns hide underneath aggregate metrics and are fragmented across business systems.??Extraordinary productivity patterns get lost when focusing squarely on average outcomes.

Rethinking Enterprise Productivity with AI

Productivity insights are only valuable when they reflect reality.??Each role has specific nuances, each employee is unique, and each team has its style, meaning aggregate insights won't cut it.??AI-driven Productivity Platforms learn about human behavior better than human managers do.??Rather than solving for individual variables or isolated parts of a process, they holistically learn how employees work, measure their impact on the overall productivity, and generate actionable insights that turn more of the hours worked into tangible results.?

This offers a more accurate representation of productivity than treating all work equally and simply reporting aggregate metrics.??And provides companies with visibility of what couldn't be measured before.?

Unsurprisingly, professional sports teams are far ahead of their?enterprise counterparts, effectively bringing analytics to bear on team performance management.??For example, in pro basketball, teams use analytics to determine what combinations of players should be on the floor during crucial moments of the game.??While key performance indicators and metrics for sporting events differ from traditional business, the essential insight remains: Team performance is not merely a sum or aggregate of individual performances.??Team performance — and the dynamics of interpersonal interaction between teams and coaches alike — requires reliable data and analytics.

The impetus driving executives to rethink enterprise productivity is the opportunity to learn from granular work dynamics – and how they play out – without the limitations of relying on average benchmarks and KPIs.??Artificial intelligence is moving from a supporting role to a central role.??The next step is to apply it more broadly to how a company is structured and operates.

Pentland argues that big data—in this case, analyzing details of interactions and behaviors on a broad scale—will reinvent what it means to have a human society.??He compares the impending transformation to the historical development of writing, education, and the Internet.

Impact on Profitability per Employee

This is the first time since the dawn of the modern corporation that we have the ability to see enough about work that we can build quantifiably more productive enterprises.??With labor costs?and shortages weighing on results, industry-leading productivity optimization platforms present a remarkable opportunity to transform how business is done to create more profitable companies.??This is a disruption on scale with the introduction of the assembly line and the revolution following the computing era.?

Reorienting data – hundreds of variables and millions of data points – around work realities?provides a more holistic, accurate perspective than pure management intuition and drives substantial value across job functions and throughout the entire process.??enaible has the unique ability to deliver results, including?19 percent extra capacity created, 28 percent decrease in variance, and?12.5 percent productivity improvement in the first year.??Through rigorous testing and deployment across multiple Fortune 500 companies, enaible is more reliable, scalable, and validated.?

Management with machine learning – purpose-built to understand and separately learn complex, contextual, and collective work – is the future of enterprise productivity.??It is a radically new way of optimizing productivity to impact the bottom line.??For every 10,000 employees, 10%?productivity growth equals $56M.

There is no question that purpose-built productivity algorithms, coupled with the near-ubiquitous digitization of work and work-related behaviors, have the potential to help companies measure, predict, and understand employee productivity at scale like never before.??